{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-nearest neighbor Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages and data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "      <th>TARGET CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.913917</td>\n",
       "      <td>1.162073</td>\n",
       "      <td>0.567946</td>\n",
       "      <td>0.755464</td>\n",
       "      <td>0.780862</td>\n",
       "      <td>0.352608</td>\n",
       "      <td>0.759697</td>\n",
       "      <td>0.643798</td>\n",
       "      <td>0.879422</td>\n",
       "      <td>1.231409</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.635632</td>\n",
       "      <td>1.003722</td>\n",
       "      <td>0.535342</td>\n",
       "      <td>0.825645</td>\n",
       "      <td>0.924109</td>\n",
       "      <td>0.648450</td>\n",
       "      <td>0.675334</td>\n",
       "      <td>1.013546</td>\n",
       "      <td>0.621552</td>\n",
       "      <td>1.492702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.721360</td>\n",
       "      <td>1.201493</td>\n",
       "      <td>0.921990</td>\n",
       "      <td>0.855595</td>\n",
       "      <td>1.526629</td>\n",
       "      <td>0.720781</td>\n",
       "      <td>1.626351</td>\n",
       "      <td>1.154483</td>\n",
       "      <td>0.957877</td>\n",
       "      <td>1.285597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.234204</td>\n",
       "      <td>1.386726</td>\n",
       "      <td>0.653046</td>\n",
       "      <td>0.825624</td>\n",
       "      <td>1.142504</td>\n",
       "      <td>0.875128</td>\n",
       "      <td>1.409708</td>\n",
       "      <td>1.380003</td>\n",
       "      <td>1.522692</td>\n",
       "      <td>1.153093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.279491</td>\n",
       "      <td>0.949750</td>\n",
       "      <td>0.627280</td>\n",
       "      <td>0.668976</td>\n",
       "      <td>1.232537</td>\n",
       "      <td>0.703727</td>\n",
       "      <td>1.115596</td>\n",
       "      <td>0.646691</td>\n",
       "      <td>1.463812</td>\n",
       "      <td>1.419167</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
       "0  0.913917  1.162073  0.567946  0.755464  0.780862  0.352608  0.759697   \n",
       "1  0.635632  1.003722  0.535342  0.825645  0.924109  0.648450  0.675334   \n",
       "2  0.721360  1.201493  0.921990  0.855595  1.526629  0.720781  1.626351   \n",
       "3  1.234204  1.386726  0.653046  0.825624  1.142504  0.875128  1.409708   \n",
       "4  1.279491  0.949750  0.627280  0.668976  1.232537  0.703727  1.115596   \n",
       "\n",
       "        PJF       HQE       NXJ  TARGET CLASS  \n",
       "0  0.643798  0.879422  1.231409             1  \n",
       "1  1.013546  0.621552  1.492702             0  \n",
       "2  1.154483  0.957877  1.285597             0  \n",
       "3  1.380003  1.522692  1.153093             1  \n",
       "4  0.646691  1.463812  1.419167             1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"Classified Data\",index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 0 to 999\n",
      "Data columns (total 11 columns):\n",
      "WTT             1000 non-null float64\n",
      "PTI             1000 non-null float64\n",
      "EQW             1000 non-null float64\n",
      "SBI             1000 non-null float64\n",
      "LQE             1000 non-null float64\n",
      "QWG             1000 non-null float64\n",
      "FDJ             1000 non-null float64\n",
      "PJF             1000 non-null float64\n",
      "HQE             1000 non-null float64\n",
      "NXJ             1000 non-null float64\n",
      "TARGET CLASS    1000 non-null int64\n",
      "dtypes: float64(10), int64(1)\n",
      "memory usage: 93.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
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       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "      <th>TARGET CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.949682</td>\n",
       "      <td>1.114303</td>\n",
       "      <td>0.834127</td>\n",
       "      <td>0.682099</td>\n",
       "      <td>1.032336</td>\n",
       "      <td>0.943534</td>\n",
       "      <td>0.963422</td>\n",
       "      <td>1.071960</td>\n",
       "      <td>1.158251</td>\n",
       "      <td>1.362725</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.289635</td>\n",
       "      <td>0.257085</td>\n",
       "      <td>0.291554</td>\n",
       "      <td>0.229645</td>\n",
       "      <td>0.243413</td>\n",
       "      <td>0.256121</td>\n",
       "      <td>0.255118</td>\n",
       "      <td>0.288982</td>\n",
       "      <td>0.293738</td>\n",
       "      <td>0.204225</td>\n",
       "      <td>0.50025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.174412</td>\n",
       "      <td>0.441398</td>\n",
       "      <td>0.170924</td>\n",
       "      <td>0.045027</td>\n",
       "      <td>0.315307</td>\n",
       "      <td>0.262389</td>\n",
       "      <td>0.295228</td>\n",
       "      <td>0.299476</td>\n",
       "      <td>0.365157</td>\n",
       "      <td>0.639693</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.742358</td>\n",
       "      <td>0.942071</td>\n",
       "      <td>0.615451</td>\n",
       "      <td>0.515010</td>\n",
       "      <td>0.870855</td>\n",
       "      <td>0.761064</td>\n",
       "      <td>0.784407</td>\n",
       "      <td>0.866306</td>\n",
       "      <td>0.934340</td>\n",
       "      <td>1.222623</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.940475</td>\n",
       "      <td>1.118486</td>\n",
       "      <td>0.813264</td>\n",
       "      <td>0.676835</td>\n",
       "      <td>1.035824</td>\n",
       "      <td>0.941502</td>\n",
       "      <td>0.945333</td>\n",
       "      <td>1.065500</td>\n",
       "      <td>1.165556</td>\n",
       "      <td>1.375368</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.163295</td>\n",
       "      <td>1.307904</td>\n",
       "      <td>1.028340</td>\n",
       "      <td>0.834317</td>\n",
       "      <td>1.198270</td>\n",
       "      <td>1.123060</td>\n",
       "      <td>1.134852</td>\n",
       "      <td>1.283156</td>\n",
       "      <td>1.383173</td>\n",
       "      <td>1.504832</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.721779</td>\n",
       "      <td>1.833757</td>\n",
       "      <td>1.722725</td>\n",
       "      <td>1.634884</td>\n",
       "      <td>1.650050</td>\n",
       "      <td>1.666902</td>\n",
       "      <td>1.713342</td>\n",
       "      <td>1.785420</td>\n",
       "      <td>1.885690</td>\n",
       "      <td>1.893950</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               WTT          PTI          EQW          SBI          LQE  \\\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean      0.949682     1.114303     0.834127     0.682099     1.032336   \n",
       "std       0.289635     0.257085     0.291554     0.229645     0.243413   \n",
       "min       0.174412     0.441398     0.170924     0.045027     0.315307   \n",
       "25%       0.742358     0.942071     0.615451     0.515010     0.870855   \n",
       "50%       0.940475     1.118486     0.813264     0.676835     1.035824   \n",
       "75%       1.163295     1.307904     1.028340     0.834317     1.198270   \n",
       "max       1.721779     1.833757     1.722725     1.634884     1.650050   \n",
       "\n",
       "               QWG          FDJ          PJF          HQE          NXJ  \\\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean      0.943534     0.963422     1.071960     1.158251     1.362725   \n",
       "std       0.256121     0.255118     0.288982     0.293738     0.204225   \n",
       "min       0.262389     0.295228     0.299476     0.365157     0.639693   \n",
       "25%       0.761064     0.784407     0.866306     0.934340     1.222623   \n",
       "50%       0.941502     0.945333     1.065500     1.165556     1.375368   \n",
       "75%       1.123060     1.134852     1.283156     1.383173     1.504832   \n",
       "max       1.666902     1.713342     1.785420     1.885690     1.893950   \n",
       "\n",
       "       TARGET CLASS  \n",
       "count    1000.00000  \n",
       "mean        0.50000  \n",
       "std         0.50025  \n",
       "min         0.00000  \n",
       "25%         0.00000  \n",
       "50%         0.50000  \n",
       "75%         1.00000  \n",
       "max         1.00000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the spread of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l=list(df.columns)\n",
    "l[0:len(l)-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run a 'for' loop to draw boxlots of all the features for '0' and '1' TARGET CLASS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d4756f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d47530748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d476a56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d4772fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d47808eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SNI0qA+BMYCtAZj4AnNo6MyJWASuBGyusQZI0jcq6gICjgGdano9GRG9mHoiIVwKfAd4N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\nBoAkFerfAQy74uyYdGzsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d477c66d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d478f1b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d4798a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d47a29908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AtcBzK6xFkjROlaeARn/YycwfR8RhLfP2Ax4EPhwRLwC+l5k51cr6+ubS29tTWbGSVJoq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\nAEkqlAEgSYUyACSpUAaAJBXq/wGfQnDrdeZ6qwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d4753fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d47545e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(l)-1):\n",
    "    sns.boxplot(x='TARGET CLASS',y=l[i], data=df)\n",
    "    plt.figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the features using sklearn.preprocessing package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Instantiate a scaler standardizing estimator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit the features data only to this estimator (leaving the TARGET CLASS column) and transform**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler.fit(df.drop('TARGET CLASS',axis=1))\n",
    "scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.123542</td>\n",
       "      <td>0.185907</td>\n",
       "      <td>-0.913431</td>\n",
       "      <td>0.319629</td>\n",
       "      <td>-1.033637</td>\n",
       "      <td>-2.308375</td>\n",
       "      <td>-0.798951</td>\n",
       "      <td>-1.482368</td>\n",
       "      <td>-0.949719</td>\n",
       "      <td>-0.643314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.084836</td>\n",
       "      <td>-0.430348</td>\n",
       "      <td>-1.025313</td>\n",
       "      <td>0.625388</td>\n",
       "      <td>-0.444847</td>\n",
       "      <td>-1.152706</td>\n",
       "      <td>-1.129797</td>\n",
       "      <td>-0.202240</td>\n",
       "      <td>-1.828051</td>\n",
       "      <td>0.636759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.788702</td>\n",
       "      <td>0.339318</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.755873</td>\n",
       "      <td>2.031693</td>\n",
       "      <td>-0.870156</td>\n",
       "      <td>2.599818</td>\n",
       "      <td>0.285707</td>\n",
       "      <td>-0.682494</td>\n",
       "      <td>-0.377850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.982841</td>\n",
       "      <td>1.060193</td>\n",
       "      <td>-0.621399</td>\n",
       "      <td>0.625299</td>\n",
       "      <td>0.452820</td>\n",
       "      <td>-0.267220</td>\n",
       "      <td>1.750208</td>\n",
       "      <td>1.066491</td>\n",
       "      <td>1.241325</td>\n",
       "      <td>-1.026987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.139275</td>\n",
       "      <td>-0.640392</td>\n",
       "      <td>-0.709819</td>\n",
       "      <td>-0.057175</td>\n",
       "      <td>0.822886</td>\n",
       "      <td>-0.936773</td>\n",
       "      <td>0.596782</td>\n",
       "      <td>-1.472352</td>\n",
       "      <td>1.040772</td>\n",
       "      <td>0.276510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
       "0 -0.123542  0.185907 -0.913431  0.319629 -1.033637 -2.308375 -0.798951   \n",
       "1 -1.084836 -0.430348 -1.025313  0.625388 -0.444847 -1.152706 -1.129797   \n",
       "2 -0.788702  0.339318  0.301511  0.755873  2.031693 -0.870156  2.599818   \n",
       "3  0.982841  1.060193 -0.621399  0.625299  0.452820 -0.267220  1.750208   \n",
       "4  1.139275 -0.640392 -0.709819 -0.057175  0.822886 -0.936773  0.596782   \n",
       "\n",
       "        PJF       HQE       NXJ  \n",
       "0 -1.482368 -0.949719 -0.643314  \n",
       "1 -0.202240 -1.828051  0.636759  \n",
       "2  0.285707 -0.682494 -0.377850  \n",
       "3  1.066491  1.241325 -1.026987  \n",
       "4 -1.472352  1.040772  0.276510  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Test split, model fit and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df_feat\n",
    "y = df['TARGET CLASS']\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features,df['TARGET CLASS'],\n",
    "                                                    test_size=0.50, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation of classification quality**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[233  17]\n",
      " [ 24 226]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "conf_mat=confusion_matrix(y_test,pred)\n",
    "print(conf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.93      0.92       250\n",
      "          1       0.93      0.90      0.92       250\n",
      "\n",
      "avg / total       0.92      0.92      0.92       500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassification error rate: 0.082\n"
     ]
    }
   ],
   "source": [
    "print(\"Misclassification error rate:\",round(np.mean(pred!=y_test),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Choosing 'k' by elbow method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "# Will take some time\n",
    "for i in range(1,60):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x28d46e31f60>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIs3jtyftauBW59zZyQfN7Drg98BemQ4sl9QNd6onTURERPzxu3BgFJBq8/Tb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\nLraZsGsXvPWWn3/+5wjHH29fNDt2hKFDw1iWj//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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d46f8fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,60),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=8)\n",
    "plt.title('Error Rate vs. K Value', fontsize=20)\n",
    "plt.xlabel('K',fontsize=15)\n",
    "plt.ylabel('Error (misclassification) Rate',fontsize=15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
